Method for organizing map data

ABSTRACT

A method of organizing map data on a physical storage medium is disclosed. The map data represents geographic features located in a geographic region. The method identifies at least one dense area in the geographic region. The map data representing the geographic features within the dense area have a data size exceeding a predetermined maximum size for a predetermined sized geographic area of the region. The method subdivides the map data representing the geographic features within the dense area into parcels so that the portion of map data contained in the parcel is close to a predetermined parcel size. The method subdivides the map data less the map data representing the dense area into parcels so that the portion of map data contained in the parcel is close to the predetermined parcel size. Additionally, the method locates the map data in each parcel together on the physical storage medium.

The present application is a continuation of application Ser. No.10/304,229 filed on Nov. 26, 2002 now U.S. Pat. No. 6,782,319, theentire disclosure of which is incorporated by reference.

BACKGROUND OF THE INVENTION

The present invention relates to a method and system for theorganization and storage of map data that facilitate use of the map databy navigation application programs such as those in navigation systems.The present invention also relates to a physical storage medium havingmap data stored thereon having the aforementioned method oforganization.

Many navigation systems use map data that are stored on read-only disks(e.g., CD-ROM disks, DVD-ROM disks, etc.) or hard disks. An importantfactor that affects the performance of some navigation system features,such as map display, is the time required to fetch the map data from thedisk. A large portion of the time required to fetch map data isattributable to the seek time of the device (i.e., the time for the readhead of the navigation system to move from a current track to the trackwhere the data are located). This factor is important for CD-ROM andDVD-ROM disks but also applies to some extent to hard disks.

Because seek time accounts for a large portion of the time required tofetch data from a disk, techniques for organizing map data have beendeveloped that reduce the number of seeks (and thus the total seek time)for a given request thereby helping to improve navigation systemperformance. One way to reduce the number of seeks when accessing mapdata for certain navigation system functions is to organize the map dataspatially. In general, when map data are organized spatially, geographicfeatures that are close together physically in the geographic region arerepresented by data records that are physically (or logically) closetogether in the map database (and possibly also close together on themedium upon which the map data are stored).

There are various methods by which data that represent geographicfeatures can be organized spatially. One of the ways that data thatrepresent geographic features can be organized spatially is to firstidentify groups of data entities that represent geographic features thatare located close together and then arrange the groups spatially. Thegroups of data may be referred to as “parcels,” “buckets,” or “mapregions,” although other terminology may also be used. The data entitieswithin each group may also be organized spatially or the data entitieswithin a group may be organized according to another arrangement.Methods for organizing map data spatially are described in U.S. Pat.Nos. 5,968,109 and 5,974,419, and U.S. patent application Ser. No.09/629,224 entitled “Method for Organizing Map Data,” filed Jul. 28,2000, the entire disclosures of which are incorporated by referenceherein.

Some map data parcelization techniques attempt to achieve uniform datasize parcels (e.g., 32 or 64 kilobytes per parcel) or fixed geographicsize parcels (e.g., 0.02 degree delta latitude by 0.03 degree deltalongitude or some other latitude/longitude “rectangle”). A disadvantageof uniform data size parcels is that when a map display rectangle spansseveral parcels that are not adjacent on the disk, several seeks arerequired to fetch the requested data, thereby possibly adverselyaffecting navigation system performance. On the other hand, adisadvantage of uniform geographic size parcels is that the data sizesof parcels vary widely from densely populated areas to sparselypopulated areas. If the area corresponding to each uniform geographicsize parcel is too small, the overhead necessary to store informationabout each parcel becomes excessive. However, if the area correspondingto uniform geographic size parcel is too large, the amount of datacontained in densely populated regions may exceed the data sizelimitations of a parcel. For example, if two-byte offsets are used tolocate byte positions of a parcel, then the inherent limit of 64kilobytes constrains the amount of data that can be stored in a parcel.

Accordingly, there is a need for an improved way to organize map datathat reduces the number of fetches needed to obtain the data required torepresent an area while accommodating wide variations in data densityacross a region.

SUMMARY OF THE INVENTION

To address these and other objectives, the present invention comprises amethod of organizing map data on a physical storage medium. The map datarepresents geographic features located in a geographic region. Themethod identifies at least one dense area in the geographic region. Themap data representing the geographic features within the dense area havea data size exceeding a predetermined maximum size for a predeterminedsized geographic area of the region. The method subdivides the map datarepresenting the geographic features within the dense area into parcelsso that the portion of map data contained in the parcel is close to apredetermined parcel size. The method subdivides the map data less themap data representing the dense area into parcels so that the portion ofmap data contained in the parcel is close to the predetermined parcelsize. Additionally, the method locates the map data in each parceltogether on the physical storage medium.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart of a parcelization method according to oneembodiment.

FIG. 2 is a grid of tiles covering a geographic region represented by ageographic database.

FIG. 3 is the grid of tiles of FIG. 2 containing large area parcels,single tile large area parcels and dense tiles.

FIG. 4 is a flow chart of a method for forming large area parcelsaccording to one embodiment.

FIG. 5 is a dense tile subdivided into parcels.

FIG. 6 is a flow chart of a method for forming small area parcels from adense tile according to one embodiment.

DETAILED DESCRIPTION OF THE PRESENTLY PREFERRED EMBODIMENTS

Parcelization is the process of dividing a map database of a geographicregion into spatial parcels. Generally, it is desired to store datarepresenting geographic features, such as roads, spatially based uponthe physical proximity of the geographic features that they represent.Data records that are physically (or logically) close together in themap database (and possibly also close together on a storage medium uponwhich the map data are stored) represent geographic features that areclose together physically in the geographic region. To spatiallyorganize data representing geographic features, the data representingthe geographic features are organized into parcels. Each parcel of dataincludes data representing features that are located physicallyproximate to each other in the geographic region. As described furtherbelow, each parcel includes data that represent physical featuresencompassed within a geographic area of a size, shape and positiondetermined by a parcelization method. A computing platform performs theparcelization method. The computing platform accesses map data stored ina general data format from a storage medium associated with thecomputing platform to perform the parcelization process thereby forminga derived database in which the map data are organized into parcels. Thederived database can be used to perform navigation-related functions.

FIG. 1 illustrates one embodiment of the parcelization method. Briefly,the parcelization method is a two level parcelization scheme. The methodfirst identifies dense geographic areas of the geographic region, suchas a metropolitan area. For convenience, the dense geographic area willbe considered as being represented by a dense portion of the geographicdatabase because the database contains a considerable amount of data torepresent the geographic features present in the dense geographic area.After identifying the dense areas, the parcelization method organizesthe data representing each of the dense areas into a plurality of smallarea parcels, and the parcelization method organizes the data of thegeographic database less the data representing the dense areas into aplurality of large area parcels. The terms large area parcel and smallarea parcel are used for convenience. Large area parcel refers to aparcel that includes data that represent physical features encompassedwithin a geographic area of a relatively large size. Small area parcelrefers to a parcel that includes data that represent physical featuresencompassed within a geographic area of a relatively small size. Largearea parcels include data that represent physical features encompassedwith a geographic area having a larger size that that of small areaparcels.

Referring to FIG. 1, the parcelization method begins by organizing thegeographic database representing the geographic region into a grid oftiles at step 10. A tile is a spatial unit by which geographic data areorganized, subdivided and stored. FIG. 2 illustrates one embodiment of aregular grid 22 of tiles 24 covering the geographic region representedby the geographic database. The grid 22, illustrated in FIG. 2, dividesthe geographic region represented by the geographic database into fixedsized, rectangular areas. Each tile 24 contains data that represent thephysical features encompassed within the geographic area defined by thetile boundaries 26 provided by the grid 22. An origin 28 of the gridbegins at the south-western-most point of the grid 22, and all of thetiles 24 may be identified in terms of their location from the origin28.

In one embodiment, the grid 22 is a global grid of tiles that dividesthe globe into a plurality of tiles. The global grid has fixed sized (indegrees) tiles following the latitude and longitudinal grid. The tileshave a north-south dimension of 0.32768 degrees (or 32 k NavigationTechnology Units (“NTUs” hereinafter)) and an east-west dimension of0.49152 degrees (or 48 k NTUs). The tiles have an actual tile dimensionsof 36.4 km by 54.6 km at the equator with the east-west dimensionsdecreasing at higher latitudes as the cosine of the latitude, reaching27.3 km at sixty degrees north, while the north-south dimension remainsconstant. For the present embodiment, the origin of the tile grid iseighty degrees south, thirty degrees west. The coordinates of the gridare always positive, increasing to the north and east. The global gridis 488 tiles north by 732 tiles east for a total of 357,216 tiles. Inthis example, the northern limit of the grid is 80.236 degrees northlatitude. The eastern limit of the grid is 30.207 degrees westlongitude. In one embodiment, the global grid is used only to definetile boundaries. An actual geographic database representing a geographicregion will comprise the tiles that cover that specific geographicregion.

After establishing the grid 22 of tiles 24, the parcelization methoduses the tiles 24 to form parcels. A parcel can be defined as one tile24, a rectangular group of tiles 24, or a rectangular subset of a tile24. Large area parcels comprise at least one tile, and small areaparcels comprise a subset of one tile. To determine how to form theparcels from the tiles 24, the desired data size for the parcel isdetermined. For example, the parcel may have a maximum data size ofapproximately 100 kilobytes or any other appropriate data size, e.g., 32kilobytes, 64 kilobytes, 128 kilobytes, etc. (When determining a datasize, some percentage may be reserved for parcel overhead.) The tilesare grouped or divided to obtain parcels having approximately thedesired data size. In one embodiment, the parcelization method groups ordivides tiles to provide parcels having a data size close to but notexceeding a maximum data size for a parcel.

Rather than trying to determine the data size of the data within theboundaries of the tile directly, a metric estimates the ultimate datasize of the data contained within a tile. The metric is a function ofthe number of data elements of various types contained in the tile. Inone embodiment, the metric is the number of nodes in the tile, and thismetric correlates fairly well with the eventual parcel data size. Themetric may also be a function of the number of any database attribute orcombination of attributes including nodes, road segments, points ofinterests, polygons representing geographic entities such as forests andlakes, polylines representing geographic entities such as rivers or anyother geographic database attribute.

Returning to FIG. 1, the parcelization method determines a minimum andmaximum metric value for the parcels at step 12. In one embodiment, thedesired parcel data size is approximately 100 k bytes, but the targetsize may be other values. The maximum and minimum metric values areselected to provide parcels having approximately the desired parcel datasize. In one embodiment, the minimum metric value is over half of themaximum metric value. In one embodiment, the goal is to haveapproximately the same metric value for each parcel in the databasewithin a factor of two. In addition to determining the maximum andminimum metric values for the parcels, the parcelization method definesa maximum aspect ratio for the parcels. The aspect ratio is the ratio ofthe largest dimension to the smallest dimension of the rectanglecomprising the parcel. A square has an aspect ratio of one. A low aspectratio for the parcels provides advantages for applications of thegeographic database. For example, the low aspect ratio for the parcelsmakes route calculation more efficient by minimizing the number ofparcels that must be accessed along a route. In one embodiment, themaximum aspect ratio for a parcel is approximately four.

Referring to FIG. 1, the parcelization method determines the metricvalue for each tile in the database at step 14. After the metric valuesare determined for all of the tiles, each metric value is compared tothe maximum metric value for a parcel. If the metric value of the tileis greater than the maximum metric value for a parcel, the methodidentifies the tile as a dense tile at step 16. The dense tile containsa larger amount of data than appropriate for a single parcel.Additionally, the dense tile contains data representing a densegeographic area of the geographic region, such as a metropolitan area.

After the dense tiles are identified, the method forms large areaparcels from rectangular groups of tiles at step 18, and the methodforms small area parcels by subdividing the dense tiles at step 20. Step18 will be described in detail below in conjunction with FIGS. 3 and 4;step 20 will be described in detail below in conjunction with FIGS. 5and 6. The steps 18 and 20 may be performed in any order.

FIG. 3 illustrates examples of large area parcels and dense tiles in thegrid 22 of tiles 24 of FIG. 2. In FIG. 3, the dense tiles 30 that needto be subdivided into small area parcels are shown as gray-shaded tovisually distinguish them. FIG. 3 also illustrates several large areaparcels 32, 34 and 36. Large area parcels 32 comprise a rectangulargroup of tiles 24. Large area parcels 36 comprise a single tile 24.Large area parcels 34 comprise a rectangular group of tiles 24 includinga dense tile 30. The dense tile 30 may be located within the geographicarea encompassed by the large area parcel 34, but the data of the densetile 30 is not included with the data of the large area parcel 34. Theeffective metric value for the large area parcel 34 is the total sum ofall the metric values of the tiles within the rectangle encompassing thelarge area parcel 34 minus the metric values for any dense tiles 30contained within the rectangle. The large area parcel 34 including thedense tile 30 may be considered as having a hole within the large areaparcel 34.

As illustrated in FIG. 3, the large area parcels 32, 34 and 36 haveboundaries that follow the grid lines 26. Thus, the large area parcels32, 34 and 36 and dense tiles 30 cover the entire database representingthe geographic region without overlapping. Additionally, because thelarge area parcels have boundaries that follow the grid lines 26, thelarge area parcels have dimensions in the east-west direction that aremultiples of the dimension of the tile in the east-west direction anddimensions in the north-south direction that are multiples of thedimension of the tile in the north-south direction.

FIG. 4 illustrates one embodiment of the parcelization method forforming the large area parcels 32, 34 and 36. Briefly, the followingmethod forms large area parcels by dividing the geographic databasealong tile boundaries. The method begins with the entire geographicregion represented by the geographic database as a single rectangle 38in FIG. 3. At step 40, the method computes the metric value of therectangle 38. The rectangle 38 has a metric value equal to the sum ofthe metric value of each tile 24 within the rectangle 38 less the metricvalues of the dense tiles 30 within the rectangle 38. Step 42 determineswhether the metric value for the rectangle 38 is less than the maximummetric value for a parcel. If the metric value of the rectangle 38 isless than the maximum metric value for a parcel, a large area parcel isformed from the rectangle 38 at step 44.

If the metric value for the rectangle 38 is larger than the maximummetric value for a parcel, the method divides the rectangle 38 alongtile boundaries 26 into two smaller rectangles at step 46. To determinealong which grid line to divide the rectangle 38, the method determineswhether the metric value of the rectangle is greater than three timesthe maximum metric value or less than three times the minimum metricvalue for a parcel. If so, the method divides the rectangle to provideapproximately half of the metric value of the rectangle on either sideof the dividing line. If not, the method divides the rectangle toprovide approximately one third of the metric value of the rectangle onone side and two thirds of the value on the other side of the dividingline. The dividing line is chosen to be parallel to the shorter side ofthe rectangle.

For the dividing step 46, the method tests the aspect ratio of the tworectangles of the contemplated division. If the division produces newrectangles that have aspect ratios larger than the maximum aspect ratiofor a parcel, a different division line is chosen. In one embodiment,the method tests the aspect ratio of each rectangle (AR_(test)) of thecontemplated division to determine whether its value is larger than theproduct of the maximum aspect ratio (AR_(max)) and the rectangle'smetric value divided by the minimum metric value for a parcel (M_(min)).In equation form the test is whether(AR_(test)>AR_(max)*metric_(test)/M_(min)). If the aspect ratio ofeither rectangle of the contemplated division is too high, the divisionline is altered. If the division was to provide approximately one thirdon one side and two thirds on the other side, the division line is movedto provide half of the calculated metric value on either side. If thedivision was to provide approximately one half of the calculated metricvalue on each side, the division line is moved toward the center of therectangle until the aspect ratio is acceptable.

In FIG. 4, after the rectangle 38 has been divided in step 46, themethod returns to step 40. The method repeats steps 40-46 for each ofthe rectangles from the division. At step 48, the method determineswhether a rectangle remains from a prior division that has not beenformed into a large area parcel. If so, steps 40-46 repeat for therectangle. If not, the process for forming large area parcels has beencompleted.

Returning to FIG. 1, the parcelization method subdivides the dense tiles30 into small area parcels at step 20. FIG. 5 illustrates a dense tile30 subdivided into rows of small area parcels 52. The dense tile 30comprises the small area parcels 52 formed along boundaries of a grid 54of subtiles 56. The grid 54 provides a plurality of fixed sized,rectangular subtiles 56 within the dense tile 30. As described above,the dense tile 30 has a north-south dimension of 0.32768 degrees (or 32k NTUs) and an east-west dimension of 0.49152 degrees (or 48 k NTUs).Each subtile 56 has a north-south dimension that is 1/16^(th) of thenorth-south dimension of the tile (or 2 k NTUs) and an east-westdimension that is 1/16^(th) of the east-west dimension of the tile (or 3k NTUs). At 45N latitude the subtile 56 is approximately 2.27 km×1.6 km.

Referring to FIG. 5, an origin 58 of the grid of subtiles 56 begins atthe south-western-most point of the grid 54, and all of the subtiles 56may be identified in terms of their location from the origin 58. Asillustrated in FIG. 5, the small area parcels 52 are arranged in rasterfashion as east-west rows with each small area parcel 52 within the samerow having equal north-south dimensions (same number of subtiles 56high). Different rows may have different north-south dimensions that aremultiples of one subtile high. Because the small area parcels 52 haveboundaries that follow the subtile grid lines 54, the small area parcels52 have dimensions in the east-west direction that are multiples of thedimension of the subtile 56 in the east-west direction. The small areaparcels 52 have dimensions in the north-south direction that aremultiples of the dimension of the subtile in the north-south direction.The maximum aspect ratio for a small area parcel 52 is again limited bya maximum value, such as four.

FIG. 6 illustrates one embodiment of the parcelization method forforming small area parcels 52 from the dense tile 30. Briefly, thefollowing method divides the dense tile 30 along subtile boundaries 54into east-west rows and then forms the small area parcels 52 by dividingthe rows along north-south subtile boundaries 54. Referring to FIG. 6,the method divides the dense tile 30 into rows at step 60. Thenorth-south dimension of the rows is chosen to allow the rows to bedivided into small area parcels 52 with acceptable aspect ratios. Areasof the dense tile 30 having dense data distributions will be made intorows having a smaller height than areas of the dense tile 30 having aless dense data distribution. According to one embodiment for dividingthe dense tile 30 into rows, the method first divides the dense tile 30into north-south slices one subtile wide. The method then computes themetric value for each of the north-south slices. If the metric value ofany of the north-south slices is greater than the maximum metric valuefor a parcel, the dense tile 30 is divided into two rows by finding theeast-west line following the subtile boundaries that providesapproximately half of the metric value on each side of the dividingline. The resulting two rows are each processed in the same manner asdescribed above. The method divides each row until none of thenorth-south slices of the resulting rows have a metric value greaterthan the maximum metric value for a parcel.

After the dense tile 30 has been divided into rows, each row will beorganized into small area parcels 52. The method chooses one of the rowsand treats the entire row as a single rectangle. At step 62, the methodcalculates the metric value for the rectangle. Step 64 determineswhether the metric value for the rectangle is less than the maximummetric value for a parcel. If the metric value of the rectangle is lessthan the maximum metric value for a parcel, the rectangle is made into asmall area parcel 52 at step 66. If the metric value for the rectangleis larger than the maximum metric value for a parcel, the rectangle isdivided into two rectangles along north-south subtile boundaries 54 atstep 68. To determine along which grid line 54 to divide the rectangle,the method determines whether the metric value of the rectangle isgreater than three times the maximum metric value for a parcel or lessthan three times the minimum metric value for a parcel. If so, therectangle is divided to provide approximately half of the metric valueon either side of the north-south dividing line. If not, the rectangleis divided to provide approximately one third of the metric value on oneside and two thirds of the metric value on the other side of thenorth-south dividing line.

For the dividing step 68, the method tests the aspect ratio of the tworectangles of the contemplated division. If the division produces newrectangles that have aspect ratios larger than the maximum aspect ratiofor a parcel, a different division line is chosen. In anotherembodiment, the method tests the aspect ratio of each rectangle(AR_(test)) of the contemplated division to determine whether its valueis larger than the product of the maximum aspect ratio (AR_(max)) andthe rectangle's metric value divided by the minimum metric value for aparcel (M_(min)). In equation form the test is whether(AR_(test)>AR_(max)*metric_(test)/M_(min)). If the aspect ratio ofeither rectangle of the contemplated division is too high, the divisionline is altered. If the division was to provide approximately one thirdon one side and two thirds of the metric value on the other side, thedivision line is moved to provide half on either side. If the divisionwas to provide approximately one half of the metric value on each side,the division line is move toward the center of the rectangle until theaspect ratio is acceptable.

In FIG. 6, after the rectangle has been divided in step 68, the methodcalculates the metric value for each of the resulting rectangles at step70 and the metric value of each rectangle is compared to the maximummetric value for a parcel at step 62. Each of the rectangles isprocessed as described above until the row has been organized into smallarea parcels 52. If no rectangles remain that have not been organizedinto small area parcels 52 at step 72, the method determines whether allof the rows of the dense tile 30 have been organized into small areaparcels 52 at step 74. If some rows remain unorganized into small areaparcels 52, the method returns to step 62 using the next row andrepeating the process for organizing the row into small area parcels 52as described above with steps 62-72. After all of the rows have beenorganized into small area parcels 52, the method has finished formingsmall area parcels 52 from the dense tile 30. All of the dense tiles 30are organized into small area parcels 52 following the steps of FIG. 6.

Once all the large area parcels 32, 34, 36 and small area parcels 52have been defined, they are organized on a physical storage media, e.g.,the CD-ROM disk, the DVD-ROM disk, hard disk or other media. (If aCD-ROM disk or DVD disk is used, it may be rewritable or notrewritable.) In one embodiment, the large area parcels and small areaparcels are organized beginning with the parcel that represents the datacontained in the rectangular area at one corner of the geographic regionrepresented by the geographic database, such as the south-west corner.The large area parcels and small area parcels are ordered such thatspatially adjacent parcels are stored close to one another so seek timeis minimized. The parcels may be ordered using Peano-N key, Morton orderor any ordering scheme. In one embodiment, if a dense tile wassubdivided, all of the small area parcels for the subdivided tile arestored before the next adjacent parcel.

Each of the large area parcels and small area parcels is identified witha unique parcel ID. In one embodiment, the parcel ID indicates thegeographic location of the parcel. For large area parcels, the parcel IDcomprises a tile address expressed as north and east deltas in tileunits from the southwest corner or origin of the geographic region. Forsmall area parcels formed by subdividing dense tiles, the parcel IDcomprises the tile address and a raster address expressed as north andeast deltas of subtile units from the southwest corner of the densetile. One bit of the ID indicates whether the parcel is a small areaparcel having both the tile address and the raster address.

Alternatives

The illustrated parcelization method described above has numerousalternatives; some of these alternative embodiments are describedbriefly below.

The above parcelization method begins by organizing the geographicdatabase representing the geographic region into a grid of tiles (seeFIG. 1, step 10 and FIG. 2). In one embodiment, the parcelization methoddivides the geographic region represented by the geographic databaseinto the grid of tiles. Alternatively, the parcelization method places apredetermined grid of tiles having a predetermined size and shape overthe geographic region. The grid described above in conjunction with FIG.2 comprises fixed size rectangular tiles. In alternative embodiments,the grid may be comprised of tiles having other shapes and other sizes.Generally, the preferred shape for the tiles is a rectangular shape,such as squares and rectangles although other shapes may be used.Alternatively, tiles may have irregular shapes. For example, the tilesmay be defined by county or municipal boundaries. The size of the tilesmay be determined depending on several factors including the size of thegeographic region represented by the geographic database, the amount ofdata in the geographic database, the number of tiles desired, thedensity of data for a metropolitan area or any other factor. In oneembodiment, the tile size is selected to be approximately an averagesize of a typical metropolitan area. In another embodiment, the tilesize is selected as a binary power of the unit of measurement to alloweasy manipulation with the computing platform. An advantage of the fixedsized rectangular tiles as depicted in FIG. 2 is the ability to easilyconvert between a location in latitude and longitude coordinates and thetile containing the location using the latitude and longitudecoordinates of the origin and the size of the tiles.

The above parcelization method identifies dense geographic areas of thegeographic region by identifying dense tiles having a metric valuelarger than the metric value for a parcel (see FIG. 1, steps 10-16).Alternatively, instead of following steps 10-16 of FIG. 1, anotherembodiment may simply identify metropolitan areas following municipalboundaries as dense areas. The alternative method may then subdivide themetropolitan areas into small area parcels in a similar manner asdescribed for the dense tiles.

In another alternative embodiment, in addition to identifying densetiles at step 16, the parcelization method identifies and forms singletile large area parcels. Single tile large area parcels comprise asingle tile. In the alternative embodiment, if the metric value for thetile is within the range of the minimum metric value and maximum metricvalue for a parcel, the tile is formed into a single tile large areaparcel.

The above parcelization method forms large area parcels by dividing thegeographic database along tile boundaries as described in conjunctionwith FIG. 4. In addition to the above method, other alternative methodsare possible for forming the large area parcels. In one alternativeembodiment, the large area parcels are formed by dividing the geographicdatabase along division lines that do not correspond with tileboundaries. In another embodiment, the large area parcels are formedfrom aggregating adjacent tiles such that the sum of metric values ofthe group of tiles is within the range of the minimum metric and maximummetric value for a parcel. In another embodiment, the large area parcelsmay have shapes other than rectangular. In yet another embodiment, thelarge area parcels may not only include the geographic area of densetiles but not the dense tile data but also the geographic area of singletile large area parcels but not the single large area tile parcel data.In this embodiment, the metric value for the rectangle of the large areaparcel is the metric value for the entire rectangle minus the metricvalue of any dense tiles and minus the metric value of any single tilelarge area parcels included in the rectangle. In a further alternativeembodiment, the large area parcels do not include the geographic area ofdense tiles.

The above parcelization method forms small area parcels by dividing thedense tile along subtile boundaries into rows of small area parcels asdescribed in conjunction with FIGS. 5 and 6. In addition to the abovemethod, other alternative methods are possible for forming the smallarea parcels. In one embodiment, the dense tile may be divided into rowsfollowing any appropriate method. In another embodiment, the parcels arenot arranged in raster fashion as east-west rows of parcels. In analternative embodiment, the small area parcels are formed by dividingthe dense tile along division lines that do not correspond with subtileboundaries. According to an alternative embodiment, the small areaparcels are formed from aggregating adjacent subtiles such that the sumof metric values of the group of subtiles is within the range of theminimum metric and maximum metric value for a parcel. In yet anotherembodiment, the small area parcels may have shapes other thanrectangular.

The above parcelization method for forming small area parcels organizesthe dense tile into a grid of subtiles (see FIG. 5). In one embodiment,the method divides the dense tile into the grid of subtiles.Alternatively, the method places a predetermined grid of subtiles havinga predetermined size and shape over the dense tile. The grid describedabove in conjunction with FIG. 5 comprises fixed size rectangularsubtiles. In alternative embodiments, the grid may be comprised ofsubtiles having other shapes and other sizes. Generally, the preferredshape for the subtiles is a rectangular shape, such as squares andrectangles, although other shapes may be used. Alternatively, subtilesmay have irregular shapes. For example, the subtiles may be defined bymunicipal boundaries or along major road segments. The size of thesubtiles may be determined depending on several factors including thesize of the geographic region represented by the dense tile, the amountof data in the dense tile, the desired data size of a parcel or anyother factor. In one embodiment, the subtile size is selected to besmall enough such that the metric value of the subtile does not exceedthe maximum metric value for a parcel. In another embodiment, thesubtile size is selected as a binary power of the unit system to alloweasy manipulation with the computing platform. An advantage of the fixedsized rectangular subtiles as depicted in FIG. 5 is the ability toeasily convert between a location in latitude and longitude coordinatesand the subtile containing the location using the latitude and longitudecoordinates of the origin and the size of the tiles and subtiles.

In another embodiment, the size of the subtile is selected to beapproximately equal to a maximum change in latitude that would bedisplayed for a given map zoom level for displaying a map from thegeographic database. For example, assume a navigation system has adisplay measuring 12 cm (horizontal) by 9 cm (vertical). At a given zoomlevel, the change in latitude that would correspond to 12 cm on thedisplay is determined. (The greater of the horizontal and verticalmeasurements is used in order to provide for reorientation of directionon the display.) This change in latitude may be approximately the heightof the subtiles.

It is intended that the foregoing detailed description be regarded asillustrative rather than limiting and that it is understood that thefollowing claims including all equivalents are intended to define thescope of the invention.

1. A method of organizing data on a physical storage medium, wherein thedata represent geographic features located in a geographic region, themethod comprising: identifying at least one dense area in the geographicregion, the data representing the geographic features within said densearea having a data size larger than a predetermined size for apredetermined sized area; forming at least one small area parcel fromthe data representing the geographic features within said dense area,and forming at least one large area parcel from the data representingthe geographic features located in the geographic region less the datarepresenting the dense area, wherein a portion of map data contained ineach small area parcel and in each large area parcel is approximatelyequal to a predetermined parcel size.
 2. The method of claim 1 whereinthe dense area is a metropolitan area.
 3. The method of claim 1 whereinthe dense area has boundaries corresponding to metropolitan areaboundaries.
 4. The method of claim 1 wherein the dense area hasboundaries following a plurality of road segments within the geographicregion.
 5. The method of claim 1 wherein a portion of map data containedin each small area parcel and in each large area parcel is less than apredetermined parcel size.
 6. The method of claim 1 wherein each of thesmall area parcels and large area parcels has rectangular shapedboundaries.
 7. The method of claim 6 wherein each of the small areaparcels and large area parcels has an aspect ratio less than apredetermined value.
 8. The method of claim 1 wherein said step ofidentifying the dense area comprising: organizing the region into a gridof tiles; for each tile, calculating a metric value estimating an amountof map data within the tile; and identifying each tile having the metricvalue greater than a maximum metric value as said dense area.
 9. Themethod of claim 1 further comprising locating the data in each smallarea parcel and large area parcel on the physical storage medium.
 10. Amap database product formed according to the method of claim
 1. 11. Anavigation system having map data organized according to the method ofclaim
 1. 12. A method of organizing map data on a physical storagemedium, the map data represent geographic features located in a region,the method comprising: organizing the region into a grid of tiles;identifying at least one dense tile having an estimated map data sizelarger than a predetermined size; forming a plurality of small areaparcels from the map data contained within the dense tile; and forming aplurality of large area parcels from the map data less the map datacontained within the dense tiles, wherein a data size of each small areaparcel and each large area parcel is approximately equal to apredetermined parcel size.
 13. The method of claim 12 wherein the tileshave a rectangular shape.
 14. The method of claim 12 wherein the tileshave a fixed size.
 15. The method of claim 12 wherein the large areaparcels comprise at least one tile.
 16. The method of claim 12 whereinthe large area parcels comprise a rectangular grouping of the tiles. 17.The method of claim 12 wherein a data size of each small area parcel andeach large area parcel is less than a predetermined parcel size.
 18. Amap database comprising: map data that represent geographic featureslocated in a region; the map data are organized into a plurality oflarge area parcels and a plurality of small area parcels; each of thesmall area parcels formed from dense areas in the region, the datarepresenting the geographic features within said dense area having adata size larger than a predetermined size for a predetermined sizedarea; each of the large area parcels formed from the data representingthe geographic features located in the region less the data representingthe dense are, wherein a data size of each small area parcel and eachlarge area parcel is approximately equal to a predetermined parcel size.